from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin




@dataclass
class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					42




class 					_UpperCAmelCase			(     lowerCAmelCase,			lowerCAmelCase  ):



 '''simple docstring'''




 @register_to_config
 def __init__(		self				:   List[str]							, lowercase_				:   int = 16							, lowercase_				:   int = 88							, lowercase_				:   Optional[int] = None							, lowercase_				:   Optional[int] = None							, lowercase_				:   int = 1							, lowercase_				:   float = 0.0							, lowercase_				:   int = 32							, lowercase_				:   Optional[int] = None							, lowercase_				:   bool = False							, lowercase_				:   Optional[int] = None							, lowercase_				:   str = "geglu"							, lowercase_				:   bool = True							, lowercase_				:   bool = True							, )						->	List[str]:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    num_attention_heads
   _UpperCamelCase           =    attention_head_dim
   _UpperCamelCase           =    num_attention_heads * attention_head_dim

   _UpperCamelCase           =    in_channels

   _UpperCamelCase           =    torch.nn.GroupNorm(num_groups=lowercase_							, num_channels=lowercase_							, eps=1e-6							, affine=lowercase_)
   _UpperCamelCase           =    nn.Linear(lowercase_							, lowercase_)

   # 3. Define transformers blocks
   _UpperCamelCase           =    nn.ModuleList(
       [
           BasicTransformerBlock(
               lowercase_							, lowercase_							, lowercase_							, dropout=lowercase_							, cross_attention_dim=lowercase_							, activation_fn=lowercase_							, attention_bias=lowercase_							, double_self_attention=lowercase_							, norm_elementwise_affine=lowercase_							, )
           for d in range(lowercase_)
       ])

   _UpperCamelCase           =    nn.Linear(lowercase_							, lowercase_)







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   List[str]							, lowercase_				:   str=None							, lowercase_				:   Dict=None							, lowercase_				:   Optional[int]=None							, lowercase_				:   List[Any]=1							, lowercase_				:   int=None							, lowercase_				:   bool = True							, )						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    hidden_states.shape
   _UpperCamelCase           =    batch_frames // num_frames

   _UpperCamelCase           =    hidden_states

   _UpperCamelCase           =    hidden_states[None, :].reshape(lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
   _UpperCamelCase           =    hidden_states.permute(0							, 2							, 1							, 3							, 4)

   _UpperCamelCase           =    self.norm(lowercase_)
   _UpperCamelCase           =    hidden_states.permute(0							, 3							, 4							, 2							, 1).reshape(batch_size * height * width							, lowercase_							, lowercase_)

   _UpperCamelCase           =    self.proj_in(lowercase_)

   # 2. Blocks
   for block in self.transformer_blocks:
     _UpperCamelCase           =    block(
         lowercase_							, encoder_hidden_states=lowercase_							, timestep=lowercase_							, cross_attention_kwargs=lowercase_							, class_labels=lowercase_							, )

   # 3. Output
   _UpperCamelCase           =    self.proj_out(lowercase_)
   _UpperCamelCase           =    (
       hidden_states[None, None, :]
       .reshape(lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_)
       .permute(0							, 3							, 4							, 1							, 2)
       .contiguous()
   )
   _UpperCamelCase           =    hidden_states.reshape(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   _UpperCamelCase           =    hidden_states + residual

   if not return_dict:
     return (output,)

   return TransformerTemporalModelOutput(sample=lowercase_)





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ = None    ,     a__ = None    ,     a__ = False    ,     )      ->tuple[int, float, str]:




  '''simple docstring'''

  _UpperCamelCase           =    cipher_alphabet or [chr(a__ ) for i in range(97    ,     123 )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    _UpperCamelCase           =    {
        "a": 0.08497,
        "b": 0.01492,
        "c": 0.02202,
        "d": 0.04253,
        "e": 0.11162,
        "f": 0.02228,
        "g": 0.02015,
        "h": 0.06094,
        "i": 0.07546,
        "j": 0.00153,
        "k": 0.01292,
        "l": 0.04025,
        "m": 0.02406,
        "n": 0.06749,
        "o": 0.07507,
        "p": 0.01929,
        "q": 0.00095,
        "r": 0.07587,
        "s": 0.06327,
        "t": 0.09356,
        "u": 0.02758,
        "v": 0.00978,
        "w": 0.02560,
        "x": 0.00150,
        "y": 0.01994,
        "z": 0.00077,
    }
  else:
    # Custom frequencies dictionary
    _UpperCamelCase           =    frequencies_dict

  if not case_sensitive:
    _UpperCamelCase           =    ciphertext.lower()

  # Chi squared statistic values
  _UpperCamelCase           =    {}

  # cycle through all of the shifts
  for shift in range(len(a__ ) ):
    _UpperCamelCase           =    ""

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        _UpperCamelCase           =    (alphabet_letters.index(letter.lower() ) - shift) % len(
            a__ )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    _UpperCamelCase           =    0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        _UpperCamelCase           =    letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.lower().count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          _UpperCamelCase           =    decrypted_with_shift.count(a__ )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          _UpperCamelCase           =    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          _UpperCamelCase           =    ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    _UpperCamelCase           =    (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(a__ ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  _UpperCamelCase           =    min(
      a__    ,     key=a__    ,     )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      _UpperCamelCase
  )      ,						(
      _UpperCamelCase
  )      ,						
  )           =    chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )




